Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	33,197
2	Florida	30,267
3	Mississippi	29,359
4	Arizona	28,308
5	Alabama	27,248
6	Georgia	25,305
7	South Carolina	24,454
8	Tennessee	23,894
9	Nevada	23,429
10	Texas	23,121
11	New York	22,890
12	Iowa	22,509
13	New Jersey	22,117
14	Arkansas	21,877
15	Rhode Island	21,326
16	District of Columbia	20,350
17	Illinois	20,141
18	Idaho	19,283
19	California	18,883
20	Nebraska	18,856
21	Maryland	18,812
22	Delaware	18,801
23	North Dakota	18,208
24	Massachusetts	17,839
25	South Dakota	17,411
26	Utah	17,287
27	North Carolina	17,081
28	Oklahoma	16,440
29	Kansas	16,434
30	Missouri	15,784
31	Indiana	15,287
32	Connecticut	15,084
33	Wisconsin	15,067
34	Virginia	15,043
35	Minnesota	14,479
36	Kentucky	12,730
37	New Mexico	12,485
38	Michigan	11,914
39	Pennsylvania	11,341
40	Ohio	11,291
41	Puerto Rico	11,116
42	Washington	10,643
43	Colorado	10,419
44	Alaska	9,117
45	Montana	7,867
46	Wyoming	7,089
47	Hawaii	7,062
48	Oregon	6,722
49	West Virginia	6,506
50	New Hampshire	5,511
51	Maine	3,506
52	Vermont	2,650

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	235
2	Kansas	209
3	South Dakota	193
4	Missouri	178
5	Tennessee	160
6	Oklahoma	157
7	Arkansas	147
8	Delaware	142
9	Iowa	140
10	Louisiana	140
11	Wisconsin	126
12	Alabama	122
13	Nebraska	115
14	Illinois	114
15	Utah	110
16	Rhode Island	109
17	Georgia	108
18	Idaho	107
19	Nevada	106
20	Puerto Rico	105
21	South Carolina	104
22	Virginia	104
23	Minnesota	102
24	Mississippi	100
25	Florida	96
26	North Carolina	96
27	Indiana	90
28	Maryland	89
29	California	84
30	Texas	82
31	Hawaii	78
32	Montana	72
33	Alaska	71
34	West Virginia	69
35	Kentucky	64
36	Ohio	62
37	District of Columbia	58
38	Michigan	57
39	Wyoming	55
40	Washington	50
41	Pennsylvania	49
42	Oregon	39
43	Connecticut	38
44	Colorado	37
45	Massachusetts	37
46	New Jersey	34
47	New York	31
48	Arizona	24
49	New Mexico	21
50	New Hampshire	17
51	Maine	11
52	Vermont	3

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,800
2	New York	1,676
3	Massachusetts	1,326
4	Connecticut	1,254
5	Louisiana	1,100
6	Rhode Island	999
7	Mississippi	868
8	District of Columbia	865
9	Arizona	717
10	Michigan	682
11	Illinois	665
12	Maryland	629
13	Delaware	625
14	Pennsylvania	614
15	South Carolina	565
16	Georgia	558
17	Florida	554
18	Indiana	502
19	Texas	477
20	Alabama	464
21	Nevada	452
22	New Mexico	384
23	Iowa	375
24	Ohio	367
25	California	350
26	Colorado	343
27	Minnesota	339
28	New Hampshire	318
29	Virginia	314
30	Arkansas	303
31	Missouri	283
32	North Carolina	280
33	Tennessee	274
34	Washington	268
35	Kentucky	232
36	Idaho	218
37	Nebraska	216
38	Oklahoma	215
39	North Dakota	209
40	Wisconsin	202
41	South Dakota	195
42	Kansas	166
43	Puerto Rico	150
44	West Virginia	139
45	Utah	132
46	Oregon	116
47	Montana	111
48	Maine	99
49	Vermont	92
50	Wyoming	72
51	Hawaii	61
52	Alaska	53

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Louisiana	5
2	Arkansas	3
3	Georgia	2
4	Iowa	2
5	South Carolina	2
6	Texas	2
7	California	1
8	Florida	1
9	Massachusetts	1
10	Mississippi	1
11	New Mexico	1
12	Ohio	1
13	Puerto Rico	1
14	Rhode Island	1
15	Tennessee	1
16	West Virginia	1
17	Alabama	0
18	Alaska	0
19	Arizona	0
20	Colorado	0
21	Connecticut	0
22	Delaware	0
23	District of Columbia	0
24	Hawaii	0
25	Idaho	0
26	Illinois	0
27	Indiana	0
28	Kansas	0
29	Kentucky	0
30	Maine	0
31	Maryland	0
32	Michigan	0
33	Minnesota	0
34	Missouri	0
35	Montana	0
36	Nebraska	0
37	Nevada	0
38	New Hampshire	0
39	New Jersey	0
40	New York	0
41	North Carolina	0
42	North Dakota	0
43	Oklahoma	0
44	Oregon	0
45	Pennsylvania	0
46	South Dakota	0
47	Utah	0
48	Vermont	0
49	Virginia	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	144,364	1	99
Lafayette	Florida	144,028	2	99
Lincoln	Arkansas	135,673	3	99
Chattahoochee	Georgia	131,934	4	99
Lake	Tennessee	120,867	5	99
Davidson	Tennessee	38,648	168	94
Richland	South Carolina	29,815	336	89
York	South Carolina	16,197	1059	66
Orange	California	16,065	1073	65
Pierce	Washington	8,731	1940	38

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,612	1	99
Galax city	Virginia	4,412	2	99
Randolph	Georgia	3,983	3	99
Terrell	Georgia	3,634	4	99
Emporia city	Virginia	3,554	5	99
Richland	South Carolina	491	704	77
Davidson	Tennessee	385	920	70
Orange	California	333	1047	66
Pierce	Washington	215	1421	54
York	South Carolina	196	1504	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.